Remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with the object. In modern usage, the term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth (both on the surface, and in the atmosphere and oceans) by means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or satellites).
TYPES OF REMOTE SENSING
There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding area being observed. Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include film photography, infrared, charge-coupled devices, and radiometers. Active collection, on the other hand, emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target. RADAR and LiDAR are examples of active remote sensing where the time delay between emission and return is measured, establishing the location, height, speed and direction of an object.
Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring deforestation in areas such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of coastal and ocean depths. Military collection during the Cold War made use of stand-off collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed.
Orbital platforms collect and transmit data from different parts of the electromagnetic spectrum, which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such as El Niño and other natural long and short term phenomena. Other uses include different areas of the earth sciences such as natural resource management, agricultural fields such as land usage and conservation, and national security and overhead, ground-based and stand-off collection on border areas.
By satellite, aircraft, spacecraft, buoy, ship, and helicopter images, data is created to analyze and compare things like vegetation rates, erosion, pollution, forestry, weather, and land use. These things can be mapped, imaged, tracked and observed. The process of remote sensing is also helpful for city planning, archaeological investigations, military observation and geomorphological surveying.
SATELLITE REMOTE SENSING AND CLIMATE PREDICTION
What can we understand and do with the application of satellite remote sensing? Some familiar examples are the distribution of sea surface temperature and the estimated depletion of the ozone hole, which we often see and hear about in our daily lives. Satellite remote sensing does not only furnish present ground surface and atmospheric conditions. For example, it is necessary to understand the mechanism of changes occurring in the atmosphere, ocean, and ground surface in order to create a model, when we attempt to project future Earth climates. Satellite remote sensing plays an important role in the creation of the model.
First, let us introduce one recent topic on climate prediction. Attention is now being paid to the interaction between clouds and aerosols as a factor in making climate prediction difficult. An aerosol is a generic term for minute particles floating in the atmosphere. These particles cool or warm the atmosphere by breaking up sunlight, or absorbing it.
In addition, aerosol acts as the cloud condensation nuclei. When aerosol behaves as the cloud condensation nuclei, the cloud particles are split up into smaller pieces. It has been noted that this phenomenon has various potential effects on the climate.
It is well known that carbon dioxide emitted by human activity causes the temperature of the atmosphere to increase. On the other hand, recent research suggests that aerosols generated by human activity transform cloud microphysical characteristics, thereby canceling temperature increases due to carbon dioxide. Satellite remote sensing is proving to be useful in accurately understanding and evaluating these effects.
UNDERSTANDING THE EARTH
We have addressed rainfall, vegetation, and oceanic primary production in the previous articles of this series. Like clouds and aerosol, these elements are also important to predict climate. Efforts are being made to improve estimation accuracy and to model the phenomena. When these phenomena are accurately modeled and integrated with a climate model, prediction accuracy is expected to gradually improve.
APPLICATION OF REMOTE SENSING ON LAND DEGRADATION
Land is the basic natural resource that provides habitant and sustenance for living organism as well as being a major focus of economic activities. Due to the negative impact on the environment and quality of life, land degradation is an important global issue (Eswaran et al., 2001).
There is no single, readily identifiable definition of land degradation, but all of them describe how one or more of the land resources (soil, water, vegetation, rocks, air, climate, relief) has changed from better to worse.
The use of Geographic Information System (GIS) and Remote Sensing techniques has been seen as one way of monitoring land degradation. Land degradation has been assessed using image classification techniques. Five classes were produced namely; dense vegetation; moderate vegetation; grasslands; stressed grasslands and bare-ground. Field verification was conducted to assess the accuracy of classification of these areas. The photographs bare-ground areas were captured. Bare-ground from remote sensing images of different years was then assessed and analysed. The results showed an increase in areas of bare-ground which highly represented soil erosion. This show one way of using remote sensing towards land degradation monitoring
Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.
APPLICATION OF REMOTE SENSING ON ROAD NETWORKS
Remote Sensing contributes most significantly to highway engineering during the reconnaissance and feasibility stage of route planning when general information is to be analyzed about large areas of terrain, rather than specific information about a small area, as would be required for example for the final alignment . Aerial photographs are one of the most popular Remote Sensing techniques owing to their supporting small scale as well as large scale surveys. Aerial photography has also been used to produce contoured topographic maps. Satellite imagery and landsat may also provide the source of Remote Sensing imagery. The scale of Landsat and IRS are ideal for reconnaissance surveys and can be appropriate for preliminary interpretation as a part of more detailed surveys. At reconnaissance and feasibility levels of survey individual geotechnical aspects of road construction are subordinate to the main aim which is to identify a route corridor. The reconnaissance or pre feasibility study helps to examine the entire area lying between the end points of a road and to identify route corridors within it. It would help to establish the relief and geology of an area, the main soil types, climatic and hydrological conditions. Remote Sensing in the form of photographic, scanning and processing systems is one of the most appropriate means of recording ground conditions and assessing their potential for engineering projects land also to evaluating the effects of subsequent construction on the environment. Remote sensing is only an aid to engineering investigations providing information which is complementary to field measurements /site visits and existing sources of data such as maps and project reports.
M.R Wigan in 1992, discovered that Image Processing techniques can successfully be used in road problems. The road texture characteristics, defect classification, automatic classification of speed and shape classification, vehicle number recognition are the various problems which can be addressed. Chester G.Wilmot et. al in 1998 applied Remote Sensing successfully to the traffic study. Through Remote Sensing the vehicle counts could be made with the help of various vehicle emissions. It has been observed that the observations taken by Remote Sensing on two-way two-lane roads are 800/hour which is approximately 80% of the observations made on one lane roads. Volume, Directional split (Percentage of traffic in the dominant direction), and traffic composition are the factors affecting the observations.
David K.Loukes and John McLaughlin were the pioneer Canadians in the field of GIS applications to transportation using various thematic data. The GIS offered the capability of linking graphic entities (discrete points, roadway links, right of way parcels, etc) to many existing attribute data base that contain information about the transportation infrastructure. The linkage mechanism is enabled through the use of carefully chosen location keys. The resultant product would be suitable as a base of subsequent GIS-T applications.
- Chester G. Wilmot et al, 1998: Validity of Remote Sensing On two Lane Roads, Journal of Transportation Engineering. Vol.1 24, No 1, pp 35-43.3.
- David K. Loukes and John McLaughlin, 1991: GIS and Transportation: Canadian Perspective, Journal of Surveying Engineering. Vol. 117 no. 3 pp 123-133.4.
- David McClung and Peter Schaerer, 1993: The Avalanche Handbook.
- Franz W. Leberl, 1982: Raster Scanning For Operational Digitizing of Graphical Data, Photogrammetric Engineering and Remote Sensing, Vol.48, pp 615-626.
- M. R. Wigan, 1992: Image Processing Techniques Applied to Road Problems. Journal of Transportation Engineering Vol.118, pp 62-81.7.
- Peter A. Bracken et al, Remote Sensing Software Systems, Manual of Remote Sensing Vol. I, pp 807-808.8.
- Peter A. Burrough and Rachael A. McDonell, 1998: Principles of GIS 9. T.J.M. Kenne et al, 1985: Remote Sensing in Civil Engineering10.Government of India, Department of Space; Annual report 2000-2001
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